German Socio-Economic Panel 1994–2002

Cross-section data for 675 14-year old children born between 1980 and 1988. The sample is taken from the German Socio-Economic Panel (GSOEP) for the years 1994 to 2002 to investigate the determinants of secondary school choice.

Usage

data("GSOEP9402")

Format

A data frame containing 675 observations on 12 variables.

school: factor. Child's secondary school level.
birthyear: Year of child's birth.
gender: factor indicating child's gender.
kids: Total number of kids living in household.
parity: Birth order.
income: Household income.
size: Household size
state: factor indicating German federal state.
marital: factor indicating mother's marital status.
meducation: Mother's educational level in years.
memployment: factor indicating mother's employment level: full-time, part-time, or not working.
year: Year of GSOEP wave.

Details

This sample from the German Socio-Economic Panel (GSOEP) for the years between 1994 and 2002 has been selected by Winkelmann and Boes (2009) to investigate the determinants of secondary school choice.

In the German schooling system, students are separated relatively early into different school types, depending on their ability as perceived by the teachers after four years of primary school. After that, around the age of ten, students are placed into one of three types of secondary school: "Hauptschule" (lower secondary school), "Realschule" (middle secondary school), or "Gymnasium" (upper secondary school). Only a degree from the latter type of school (called Abitur) provides direct access to universities.

A frequent criticism of this system is that the tracking takes place too early, and that it cements inequalities in education across generations. Although the secondary school choice is based on the teachers' recommendations, it is typically also influenced by the parents; both indirectly through their own educational level and directly through influence on the teachers.

Source

Online complements to Winkelmann and Boes (2009).

References

Winkelmann, R., and Boes, S. (2009). Analysis of Microdata, 2nd ed. Berlin and Heidelberg: Springer-Verlag.

Examples

#> Loading required namespace: effects
## data
data("GSOEP9402", package = "AER")

## some convenience data transformations
gsoep <- GSOEP9402
gsoep$year2 <- factor(gsoep$year)

## visualization
plot(school ~ meducation, data = gsoep, breaks = c(7, 9, 10.5, 11.5, 12.5, 15, 18))



## Chapter 5, Table 5.1
library("nnet")
gsoep_mnl <- multinom(
  school ~ meducation + memployment + log(income) + log(size) + parity + year2,
  data = gsoep)
#> # weights:  48 (30 variable)
#> initial  value 741.563295 
#> iter  10 value 655.748279
#> iter  20 value 624.992858
#> iter  30 value 618.605354
#> final  value 618.475696 
#> converged
coeftest(gsoep_mnl)[c(1:6, 1:6 + 14),]
#>                                   Estimate Std. Error    z value     Pr(>|z|)
#> Realschule:(Intercept)          -6.3864877 2.36903996 -2.6958126 7.021716e-03
#> Realschule:meducation            0.3004843 0.07910641  3.7984819 1.455851e-04
#> Realschule:memploymentparttime   0.4933680 0.32189721  1.5326879 1.253528e-01
#> Realschule:memploymentnone       0.7526399 0.32884476  2.2887392 2.209451e-02
#> Realschule:log(income)           0.3934871 0.22539836  1.7457408 8.085601e-02
#> Realschule:log(size)            -1.1921790 0.44641156 -2.6705827 7.571972e-03
#> Realschule:year22002             0.1922413 0.45158350  0.4257049 6.703229e-01
#> Gymnasium:(Intercept)          -23.6975758 3.01022807 -7.8723523 3.480345e-15
#> Gymnasium:meducation             0.6597649 0.08144034  8.1012060 5.441700e-16
#> Gymnasium:memploymentparttime    0.9372429 0.34536421  2.7137813 6.652007e-03
#> Gymnasium:memploymentnone        1.1007579 0.35842760  3.0710746 2.132898e-03
#> Gymnasium:log(income)            1.6676745 0.28408439  5.8703492 4.348783e-09
 
## alternatively
library("mlogit")
gsoep_mnl2 <- mlogit(
  school ~ 0 | meducation + memployment + log(income) + log(size) + parity + year2,
  data = gsoep, shape = "wide", reflevel = "Hauptschule")
coeftest(gsoep_mnl2)[1:12,]
#>                                   Estimate Std. Error   t value     Pr(>|t|)
#> (Intercept):Gymnasium          -23.6982768 3.01026604 -7.872486 1.475202e-14
#> (Intercept):Realschule          -6.3865987 2.36904833 -2.695850 7.204061e-03
#> meducation:Gymnasium             0.6597829 0.08144157  8.101304 2.726719e-15
#> meducation:Realschule            0.3004923 0.07910725  3.798543 1.593085e-04
#> memploymentparttime:Gymnasium    0.9372401 0.34536576  2.713761 6.830145e-03
#> memploymentparttime:Realschule   0.4933644 0.32189760  1.532675 1.258463e-01
#> memploymentnone:Gymnasium        1.1007670 0.35842942  3.071084 2.222541e-03
#> memploymentnone:Realschule       0.7526490 0.32884523  2.288764 2.241551e-02
#> log(income):Gymnasium            1.6677258 0.28408738  5.870468 6.954975e-09
#> log(income):Realschule           0.3934899 0.22539876  1.745750 8.133056e-02
#> log(size):Gymnasium             -1.5459256 0.48775919 -3.169444 1.599570e-03
#> log(size):Realschule            -1.1921835 0.44641174 -2.670592 7.762668e-03

## Table 5.2
library("effects")
#> lattice theme set by effectsTheme()
#> See ?effectsTheme for details.
gsoep_eff <- effect("meducation", gsoep_mnl,
  xlevels = list(meducation = sort(unique(gsoep$meducation))))
gsoep_eff$prob
#>       prob.Hauptschule prob.Realschule prob.Gymnasium
#>  [1,]      0.686724467      0.24514452     0.06813102
#>  [2,]      0.494486442      0.32195219     0.18356137
#>  [3,]      0.385007121      0.33853566     0.27645721
#>  [4,]      0.331068546      0.33830072     0.33063074
#>  [5,]      0.279605513      0.33203209     0.38836239
#>  [6,]      0.231922018      0.32005576     0.44802222
#>  [7,]      0.189019319      0.30313717     0.50784351
#>  [8,]      0.119575666      0.25898492     0.62143941
#>  [9,]      0.093066080      0.23424613     0.67268779
#> [10,]      0.071541719      0.20926168     0.71919660
#> [11,]      0.054404764      0.18493393     0.76066131
#> [12,]      0.040990572      0.16192466     0.79708477
#> [13,]      0.022753542      0.12138826     0.85585820
#> [14,]      0.006601958      0.06423885     0.92915919
plot(gsoep_eff, confint = FALSE)


## omit year
gsoep_mnl1 <- multinom(
  school ~ meducation + memployment + log(income) + log(size) + parity,
  data = gsoep)
#> # weights:  24 (14 variable)
#> initial  value 741.563295 
#> iter  10 value 658.442291
#> iter  20 value 624.980518
#> final  value 624.957624 
#> converged
lrtest(gsoep_mnl, gsoep_mnl1)
#> Likelihood ratio test
#> 
#> Model 1: school ~ meducation + memployment + log(income) + log(size) + 
#>     parity + year2
#> Model 2: school ~ meducation + memployment + log(income) + log(size) + 
#>     parity
#>   #Df  LogLik  Df  Chisq Pr(>Chisq)
#> 1  30 -618.48                      
#> 2  14 -624.96 -16 12.964     0.6754


## Chapter 6
## Table 6.1
library("MASS")
gsoep_pop <- polr(
  school ~ meducation + I(memployment != "none") + log(income) + log(size) + parity + year2,
  data = gsoep, method = "probit", Hess = TRUE)
gsoep_pol <- polr(
  school ~ meducation + I(memployment != "none") + log(income) + log(size) + parity + year2,
  data = gsoep, Hess = TRUE)

## compare polr and multinom via AIC
gsoep_pol1 <- polr(
  school ~ meducation + memployment + log(income) + log(size) + parity,
  data = gsoep, Hess = TRUE)
AIC(gsoep_pol1, gsoep_mnl)
#>            df      AIC
#> gsoep_pol1  8 1275.075
#> gsoep_mnl  30 1296.951

## effects
eff_pol1 <- allEffects(gsoep_pol1)
plot(eff_pol1, ask = FALSE, confint = FALSE)



## More examples can be found in:
## help("WinkelmannBoes2009")